Amazon Lex Documentation

Amazon Lex is an artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy voice and text conversational interfaces in applications. Lex integrates with AWS Lambda, used to easily trigger functions for execution of your back-end business logic for data retrieval and updates. Once built, your bot can be deployed directly to contact centers, chat and text platforms, and IoT devices. Lex provides rich insights and pre-built dashboards to track metrics.

Builders can enable conversational answers to commonly asked customer questions by leveraging a new intent type that queries an authorized knowledge source and utilizes foundation models from Amazon Bedrock to provide an accurate response. This retrieval augmented generation (RAG) based solution provides a customized, conversational request and response framework that allows customers to get automated answers quickly and reliably, and improve self-service further.

This feature leverages large language models (LLM) to resolve slot values when the native NLU is unable to. Customers can provide free form responses to basic questions and the LLM’s enhanced reasoning capabilities can resolve the response to a specified format understood by the slot definitions. 

This Gen AI based feature allows bot developers to create a bot based on a user’s prompt. Simply provide a description in natural language to generate a baseline bot which can be further refined. 

Builders can now generate variations of sample utterances to improve intent classification accuracy with minimal effort.

Amazon Lex provides automatic speech recognition and natural language understanding technologies to create a Speech Language Understanding system. Amazon Lex is able to learn the multiple ways users can express their intent based on sample utterances. The speech language understanding systems takes natural language speech and text input, understands the intent behind the input, and fulfills the user intent by invoking the appropriate response.

As the conversation develops, being able to classify utterances accurately requires managing context across multi-turn conversations. Amazon Lex supports context management natively, so you can manage the context directly without the need for custom code. As initial prerequisite intents are filled, you can create “contexts” to invoke related intents. This simplifies bot design and expedites the creation of conversational experiences.

Amazon Lex bots provide the ability for multi-turn conversations. Once an intent has been identified, users can be prompted for information that is required for the intent to be fulfilled. Amazon Lex allows you to build multi-turn conversations for your chatbots—you simply list the slots/parameters you want to collect from your bot users, as well as the corresponding prompts, and Amazon Lex can orchestrate the dialogue by prompting for the appropriate slot.

The Visual Conversation Builder in the Amazon Lex console is a drag-and-drop conversation builder that can accelerate bot building. Simply connect conversation nodes and easily iterate and test conversation designs in a no-code environment. It empowers any user to quickly build sophisticated and natural automated interactions, view conversation intent at a glance, and get visual feedback as changes are made.

Natural conversations are punctuated with pauses and interruptions. With streaming conversation APIs, you can pause a conversation and handle interruptions directly as you configure the bot. You can quickly enhance the conversational capability of virtual contact center agents or smart assistants.

Amazon Lex V2 offers an Automated Chatbot Designer that simplifies bot design by utilizing existing conversation transcripts. The designer analyzes the transcripts and proposes an initial bot design with intents and slot types. You can then customize the design by adding prompts, testing the bot, and deploying it.

Test workbench enables you to author and execute test sets to measure bot performance as you add new use cases and updates. After changes, test workbench helps to ensure your bot meets the performance criteria by having Lex generate audio and text test sets from previous user interactions. Lex can then provide aggregated results and present detailed insights into speech transcription, intent matching, and slot resolution.

Lex Analytics gives you access to prebuilt dashboards detailing key metrics such as the number of total conversations and intent recognition rates. Analytics can help you better understand where in the conversation people are failing and how users navigate across intents.

Amazon Lex lets you apply versioning to the intents, slot types, and bots that you create. Versioning and rollback mechanisms enables you to easily maintain code as you test and deploy in a multi-developer environment. You can create multiple aliases for each Amazon Lex bot and associate different versions to each such as “production,” “development,” and “test”. This allows you to continue making improvements and changes to the bot and release them as new versions under one alias. This removes the need to update all the clients when a new version of the bot is deployed.

Amazon Lex allows you to easily publish your bot to chat services directly from the Amazon Lex console, reducing multi-platform development efforts. Rich formatting capabilities provide an intuitive user experience tailored to chat platforms like Facebook Messenger, Slack, and Twilio SMS.

Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. You can use Polly to respond to your users in speech interactions. In addition to Standard TTS voices, Amazon Polly offers Neural Text-to-Speech (NTTS) voices that deliver advanced improvements in speech quality through a new machine learning approach.

Amazon Lex natively supports integration with AWS Lambda for data retrieval, updates, and business logic execution. The serverless compute capacity allows effortless execution of business logic at scale while you focus on developing bots. From Lambda, you can use AWS Lambda to easily integrate with your existing enterprise applications and databases. You just write your integration code and AWS Lambda automatically runs your code when needed to send or retrieve data from any external system. You can also access various AWS services, such as Amazon DynamoDB for persisting conversation state and Amazon SNS for notifying end users.

Amazon Kendra provides you with a highly accurate and easy-to-use intelligent search service powered by machine learning. You can add a Kendra search intent to find answers from unstructured documents and FAQs. You simply define the search index parameters in the intent as part of the bot definition to expand its informational capabilities.

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see https://docs.thinkwithwp.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://thinkwithwp.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.